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413 points martinald | 1 comments | | HN request time: 0s | source
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simonw ◴[] No.46198601[source]
The cost of writing simple code has dropped 90%.

If you can reduce a problem to a point where it can be solved by simple code you can get the rest of the solution very quickly.

Reducing a problem to a point where it can be solved with simple code takes a lot of skill and experience and is generally still quite a time-consuming process.

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loandbehold ◴[] No.46198714[source]
Most of software work is maintaining "legacy" code, that is older systems that have been around for a long time and get a lot of use. I find Claude Code in particular is great at grokking old code bases and making changes to it. I work on one of those old code bases and my productivity increased 10x mostly due to Claude Code's ability to research large code bases, make sense of it, answer questions and making careful surgical changes to it. It also helps with testing and debugging which is huge productivity boost. It's not about its ability to churn out lots of code quickly: it's an extra set of eyes/brain that works much faster that human developer.
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zmmmmm ◴[] No.46200183[source]
I've found this as well. In some cases we aren't fully authorised to use the AI tools for actual coding but even just asking "how would you make this change" or "where would you look to resolve this bug" or "give me an overview of how this process works" is amazingly helpful.
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eru ◴[] No.46200779[source]
> In some cases we aren't fully authorised to use the AI tools for actual coding but even just asking "how would you make this change" [...]

Isn't the logical endpoint of this equivalent to printing out a Stackoverflow answer and manually typing it into your computer instead of copy-and-pasting?

Nitpicks aside, I agree that contemporary AIs can be great for quickly getting up to speed with a code base. Both a new library or language you want to be using, and your own organisation's legacy code.

One of the biggest advantages of using established ecosystem was that stack-overflow had a robust repository of already answered questions (and you could also buy books on it). With AI you can immediately cook up your own Stackoverflow community equivalent that provides answers promptly instead of closing your question as off-topic.

And I pick Stackoverflow deliberately: it's a great resources, but not reliable enough to use blindly. I feel we are in a similar situation with AI at the moment. This will change gradually as the models become better. Just like Stackoverflow required less expertise to use than attending a university course. (And a university course requires less expertise than coming up with QuickSort in the first place.)

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1. jordanbeiber ◴[] No.46201763{3}[source]
The problem is most likely not writing the actual code, but rather understanding an old, fairly large codebase and how it’s stitched together.

SO is (was?) great when you where thinking about how nice a recursive reduce function could replace the mess you’ve just cobbled together, but language x just didn’t yet flow naturally for you.